mesh-flow (version 2) for general ai agent (openclaw, hermes agent)

    2

    Replace fragile prompt-chains with a strict, artifact-driven DAG orchestration system for reliable agent workflows.

    $8

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    Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    mesh-flow (version 2) for general ai agent (openclaw, hermes agent)

    Example session with this skill installed

    Compile project.yaml for the contract-to-verify pipeline and execute the first node once validated.

    • Read your context and instructions
    • Compiled the mesh-flow version 2

    Compiled execution plan from project.yaml:

    • Success: 5 nodes validated
    • Graph: contract -> plan -> (implement, review) -> verify
    • Gates: [human_approval] detected on 'review' node.
      Execution: Node [draft-plan] started. Consuming [contract]. Success. Produced [plan].
    Compiled execution plan from project.yaml

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Artifact-Driven DAG Orchestration

    Complexity in AI agents often stems from implicit prompt-chaining where flow logic is buried inside instructions. mesh-flow solves this by introducing a strict, compile-then-run DAG (Directed Acyclic Graph) architecture. It decouples the flow topology from individual node reasoning, ensuring your agents follow a predictable, reproducible, and verifiable path.

    What it does

    This skill provides a robust framework for building complex agentic workflows using artifact-driven modeling. Instead of telling an agent to "then do X," you define what artifacts a node consumes and what it produces. The system uses a dedicated CLI to validate dependencies, detect cycles, and compile your YAML definitions into a normalized execution plan.

    • Explicit Topology: Uses project.yaml as the single source of truth for your flow logic.
    • Hard Gates: Enforces runtime logic (like human approval or upstream success) that prompts cannot hallucinate their way through.
    • State Machine Execution: Manages node states (failed, blocked, rejected) with explicit recovery paths.
    • Standardized Tracing: Generates detailed execution traces for every node, including prompt templates and tool calls.

    Why use this skill

    Unlike standard prompting, mesh-flow provides a structural "spine" for your agents. It prevents flow drift, enables shadow-mode testing for complex migrations, and provides a CLI for local validation and Mermaid diagram generation. It is ideal for developers building multi-step pipelines where reliability and auditability are non-negotiable.

    • Requires local Node.js environment for CLI validation.
    • Not designed for dynamic, runtime-generated DAG branches.
    • No native GUI; visualization requires Mermaid support.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Requires

    tsx
    Node.js

    Permissions

    Terminal / Shell
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    Listed6 months ago

    What's inside

    Frequently Asked Questions

    Mesh-flow replaces fragile, prompt-based chaining with a strict DAG (Directed Acyclic Graph) architecture. It solves "flow drift" by decoupling logic from prompts, ensuring agents follow a predictable, verifiable path via artifact-driven dependencies.

    Version 2 is optimized for the OpenClaw and Hermes Agent ecosystems. It is compatible with any AI agent that supports YAML-based configuration and external CLI-based orchestration calls.

    You receive the core mesh-flow orchestration engine, the dedicated CLI for flow validation, several starter templates for project.yaml, and documentation for generating flow diagrams via Mermaid.

    Since it uses a "compile-then-run" approach, you can run the mesh-flow CLI locally to validate hardware dependencies, detect logic cycles, and visualize your workflow before deploying it to a live agent.

    Mesh-flow uses "hard gates" for state management (failed, blocked, or rejected). Unlike prompt-only agents, mesh-flow enforces explicit recovery paths and human-in-the-loop approvals that cannot be bypassed by model hallucinations.

    New in Agents & Orchestration